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Abduction and Induction Based on Non-Monotonic Reasoning

  • F. Bergadano
  • Ph. Besnard
Conference paper
Part of the International Centre for Mechanical Sciences book series (CISM, volume 363)

Abstract

We consider abduction and induction in Artificial Intelligence, focussing on one particular case for each, namely diagnostic and learning. Overall, we formalize abduction as well as induction as two syntactic specializations of a single reasoning scheme, leading from observed consequences to plausible hypotheses. The problem of finding hypotheses that justify given facts is then transformed into an inference, leading from these very facts, and from prior relevant background knowledge, to the corresponding hypotheses. This is found to be actually a form of non-monotonic inference, amenable to some appropriate non-monotonic logic, where the direction of reasoning is reversed: A logic of “reversing implication” is obtained. We give a sequent system for that logic, with application to both an example of abduction and an example of induction.

Keywords

Order Logic Inductive Reasoning Light Bulb Sequent System Deductive Reasoning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Wien 1995

Authors and Affiliations

  • F. Bergadano
    • 1
  • Ph. Besnard
    • 2
  1. 1.University of MessinaMessinaItaly
  2. 2.IRISARennesFrance

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